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Technical Committee 6: Computational Electromagnetics Computational Electromagnetics, Optimization, Direct and Inverse Design, Artificial Intelligence

Roles

The roles of the AP-S TC on Computational Electromagnetics (CEM) are to advance the theoretical, numerical and applied aspects of computational methods for solving electromagnetic problems in antennas and propagation, as well as in related areas of science and technology that leverage electromagnetic effects. The committee fosters collaboration among researchers, practitioners, and industries by organizing special sessions, workshops, and panels at IEEE AP-S conferences and related events. It promotes the development of standardized benchmarks, open datasets, and reproducible problems to enable fair comparisons and accelerate innovation in CEM. The TC also supports the integration of emerging technologies, such as artificial intelligence, quantum computing, and multi-physics simulation, into computational electromagnetics. Additionally, it aims to recognize and disseminate novel contributions, encourage diversity and inclusivity within the community, and bridge gaps between academia and industry for practical applications of CEM advancements.

 

Scope

  1. All aspects of numerical techniques for solving Maxwell’s Equations in the time and frequency domains.
  2. High-performance computing in CEM, including hardware acceleration and parallelization strategies for large-scale problems.
  3. Stochastic models and statistical analysis for addressing various uncertainties in electromagnetic problems.
  4. Optimization methodologies in direct and inverse electromagnetic design, including applications in antenna synthesis, metamaterial design, and scattering control.
  5. Numerical methods for solving nonlinear problems and inverse problems in electromagnetics.
  6. Multiphysics simulation methodologies integrating electromagnetic phenomena with other physical domains, such as thermal, mechanical, or quantum effects.
  7. AI/ML-assisted surrogate modeling, optimization, and model reduction for electromagnetics.
  8. Quantum computing and optimization methods applied to electromagnetic problems.
  9. Benchmarking, validation, and reproducibility in computational electromagnetics.